scientific article; zbMATH DE number 1420699
From MaRDI portal
zbMath0953.62049MaRDI QIDQ4944751
Publication date: 22 March 2000
Title: zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Asymptotic properties of nonparametric inference (62G20) Order statistics; empirical distribution functions (62G30) Research exposition (monographs, survey articles) pertaining to statistics (62-02)
Related Items
On least squares estimation under heteroscedastic and heavy-tailed errors, Isotonic regression with unknown permutations: statistics, computation and adaptation, Interpreting uninterpretable predictors: kernel methods, Shtarkov solutions, and random forests, \(M\)-estimators for regression with changing scale, Analysis of the Generalization Error: Empirical Risk Minimization over Deep Artificial Neural Networks Overcomes the Curse of Dimensionality in the Numerical Approximation of Black--Scholes Partial Differential Equations, Sampling discretization and related problems, Nonparametric quasi-maximum likelihood estimation for Gaussian locally stationary processes, Efficient independent component analysis, Convergence rates of deep ReLU networks for multiclass classification, The stochastic convergence of Bernstein polynomial estimators in a triangular array, Minoration via mixed volumes and Cover's problem for general channels, Inference in finite state space non parametric hidden Markov models and applications, Smoothing Splines Approximation Using Hilbert Curve Basis Selection, Exact adaptive pointwise drift estimation for multidimensional ergodic diffusions, SCAD-penalized regression in additive partially linear proportional hazards models with an ultra-high-dimensional linear part, Density estimation with stagewise optimization of the empirical risk, Statistical estimation with model selection, A solution to Hammer's X-ray reconstruction problem, Classifiers of support vector machine type with \(\ell_1\) complexity regularization, Nonparametric curve estimation with missing data: a general empirical process approach, On the strong consistency of asymptotic \(M\)-estimators, Stochastic Convergence of Regularized Solutions and Their Finite Element Approximations to Inverse Source Problems, A weighted localization of halfspace depth and its properties, Asymptotics of maximum likelihood estimation for stable law with continuous parameterization, A group bridge approach for component selection in nonparametric accelerated failure time additive regression model, Complex sampling designs: uniform limit theorems and applications, Imaging conductivity from current density magnitude using neural networks*, Full error analysis for the training of deep neural networks, Near-Linear Time Local Polynomial Nonparametric Estimation with Box Kernels, Rates of the strong uniform consistency for the kernel-type regression function estimators with general kernels on manifolds, The limiting behavior of isotonic and convex regression estimators when the model is misspecified, Statistical inference using regularized M-estimation in the reproducing kernel Hilbert space for handling missing data, Nonparametric estimation for a current status right‐censored data model, Fast convergence rates of deep neural networks for classification, Posterior contraction rate for non-parametric Bayesian estimation of the dispersion coefficient of a stochastic differential equation, Concentration behavior of the penalized least squares estimator, Functional Finite Mixture Regression Models, Semiparametric regression analysis of doubly-censored data with applications to incubation period estimation, Overall error analysis for the training of deep neural networks via stochastic gradient descent with random initialisation, Laplace priors and spatial inhomogeneity in Bayesian inverse problems, General \(M\)-estimator processes and their \(m\) out of \(n\) bootstrap with functional nuisance parameters, Latent Network Structure Learning From High-Dimensional Multivariate Point Processes, Estimating Optimal Infinite Horizon Dynamic Treatment Regimes via pT-Learning, IDENTIFICATION OF JOINT DISTRIBUTIONS IN DEPENDENT FACTOR MODELS, Optimal estimation and computational limit of low-rank Gaussian mixtures, Deep nonparametric regression on approximate manifolds: nonasymptotic error bounds with polynomial prefactors, Noisy linear inverse problems under convex constraints: exact risk asymptotics in high dimensions, A central limit theorem for the Hellinger loss of Grenander‐type estimators, Tractability from overparametrization: the example of the negative perceptron, On the rate of convergence of a deep recurrent neural network estimate in a regression problem with dependent data, On density and regression estimation with incomplete data, Stochastic Convergence of a Nonconforming Finite Element Method for the Thin Plate Spline Smoother for Observational Data, Confidence sets in sparse regression, Calibration of self-decomposable Lévy models, Efficient semiparametric estimation in generalized partially linear additive models for longitudinal/clustered data, On the uniform convergence of empirical norms and inner products, with application to causal inference, Phase retrieval for characteristic functions of convex bodies and reconstruction from covariograms, Simultaneous adaptation to the margin and to complexity in classification, The local geometry of finite mixtures, Optimal third root asymptotic bounds in the statistical estimation of thresholds, A dynamic look-ahead Monte Carlo algorithm for pricing Bermudan options, Unnamed Item, Rate-optimal estimation for a general class of nonparametric regression models with unknown link functions, Randomized Optimal Stopping Algorithms and Their Convergence Analysis, Convergence of linear functionals of the Grenander estimator under misspecification, On the rate of convergence of the maximum likelihood estimator in Brownian semimartingale models, Concentration inequalities and asymptotic results for ratio type empirical processes, Unnamed Item, Sampling discretization error of integral norms for function classes, Convergence of algorithms for reconstructing convex bodies and directional measures, Iterative isotonic regression, Robust finite mixture regression for heterogeneous targets, The uniform law of large numbers for the Kaplan-Meier integral process, Convergence and sparsity of Lasso and group Lasso in high-dimensional generalized linear models, Conditional probability estimation based classification with class label missing at random, A Bayesian nonparametric approach to log-concave density estimation, Efficient estimation in single index models through smoothing splines, Metric Entropy for Functions of Bounded Total Generalized Variation, Semiparametric \(M\)-estimation with non-smooth criterion functions, A vine copula approach for regression analysis of bivariate current status data with informative censoring, l1Penalty for Ill-Posed Inverse Problems, On Empirical Processes for Ergodic Diffusions and Rates of Convergence of M‐estimators, GENERALIZED ADDITIVE PARTIAL LINEAR MODELS WITH HIGH-DIMENSIONAL COVARIATES, Regression analysis of informatively interval-censored failure time data with semiparametric linear transformation model, A Computational Framework for Multivariate Convex Regression and Its Variants, Convergence rates of least squares regression estimators with heavy-tailed errors, Regression models for group testing: identifiability and asymptotics, Adjustments to Computer Models via Projected Kernel Calibration, Isotonic regression in general dimensions, Mean estimation and regression under heavy-tailed distributions: A survey, Localizing differentially evolving covariance structures via scan statistics, Learning rates for partially linear support vector machine in high dimensions, Asymptotic Properties of Stationary Solutions of Coupled Nonconvex Nonsmooth Empirical Risk Minimization, Asymptotic properties of semiparametric \(M\)-estimators with multiple change points, SCAD-penalised generalised additive models with non-polynomial dimensionality, Variable selection in high-dimensional partly linear additive models, Distribution-free robust linear regression, Upper functions for \(\mathbb{L}_{p}\)-norms of Gaussian random fields, Strong convergence of estimators in nonlinear autoregressive models, How well can a regression function be estimated if the distribution of the (random) design is concentrated on a finite set?, Consistent covariate selection and post model selection inference in semiparametric regression., Optimal classification and nonparametric regression for functional data, Nonparametric regression with current status data, Partially linear structure identification in generalized additive models with NP-dimensionality, Variable selection for generalized odds rate mixture cure models with interval-censored failure time data, Optimal aggregation of classifiers in statistical learning., Global rates of convergence of the MLEs of log-concave and \(s\)-concave densities, Approximation and estimation of \(s\)-concave densities via Rényi divergences, An improved global risk bound in concave regression, Efficient estimation for semiparametric varying-coefficient partially linear regression models with current status data, Statistical inference for nonparametric GARCH models, Maximum smoothed likelihood estimation and smoothed maximum likelihood estimation in the current status model, Optimal estimation in additive regression models, Adaptive penalized M-estimation with current status data, Rho-estimators for shape restricted density estimation, Upper functions for positive random functionals. I: General setting and Gaussian random functions, \(M\)-processes and applications, From \(\varepsilon\)-entropy to KL-entropy: analysis of minimum information complexity density estima\-tion, On regularization algorithms in learning theory, Inverse statistical learning, Adaptive confidence sets in \(L^2\), \(\ell^{1}\) sparsity and applications in estimation, Bootstrap consistency for general semiparametric \(M\)-estimation, Variable selection for high-dimensional varying coefficient partially linear models via nonconcave penalty, Nonparametric estimation of multivariate elliptic densities via finite mixture sieves, Nonparametric (smoothed) likelihood and integral equations, Model selection in nonparametric regression, \(\ell_{1}\)-penalization for mixture regression models, Empirical risk minimization for heavy-tailed losses, Sieve M-estimation for semiparametric varying-coefficient partially linear regression model, On the rates of convergence of simulation-based optimization algorithms for optimal stopping problems, Inference on power law spatial trends, Efficient estimation of a varying-coefficient partially linear binary regression model, On the weak convergence and central limit theorem of blurring and nonblurring processes with application to robust location estimation, Tail index estimation, concentration and adaptivity, Nonparametric regression with filtered data, Convergence of nonparametric functional regression estimates with functional responses, On the empirical estimation of integral probability metrics, Oracle inequalities for cross-validation type procedures, Global rates of convergence of the MLE for multivariate interval censoring, General oracle inequalities for model selection, Jump estimation in inverse regression, Theoretical properties of the log-concave maximum likelihood estimator of a multidimensional density, Convergence of functional \(k\)-nearest neighbor regression estimate with functional responses, Semiparametric additive transformation model under current status data, Least squares after model selection in high-dimensional sparse models, A new approach to estimator selection, Optimal rates of convergence for convex set estimation from support functions, Nonparametric regression with martingale increment errors, Semi-parametric regression: efficiency gains from modeling the nonparametric part, Adaptive risk bounds in unimodal regression, Asymptotic properties of M-estimators in linear and nonlinear multivariate regression models, General \(M\)-estimation and its bootstrap, Metric and probabilistic information associated with Fredholm integral equations of the first kind, Efficiency of profile likelihood in semi-parametric models, Uniform bounds for norms of sums of independent random functions, Support vector machines with a reject option, Estimating multiplicative and additive hazard functions by kernel methods, A uniform \(L^1\) law of large numbers for functions of i.i.d. random variables that are translated by a consistent estimator, Current status data with competing risks: consistency and rates of convergence of the MLE, The geometry of hypothesis testing over convex cones: generalized likelihood ratio tests and minimax radii, General frequentist properties of the posterior profile distribution, Rho-estimators revisited: general theory and applications, The landscape of empirical risk for nonconvex losses, Robust Bayes-like estimation: rho-Bayes estimation, Convergence rates for the generalized Fréchet mean via the quadruple inequality, On efficiency of the plug-in principle for estimating smooth integrated functionals of a nonincreasing density, A high-dimensional Wilks phenomenon, On the minimal penalty for Markov order estimation, The uniform laws of large numbers for the tent map, Risk estimators for choosing regularization parameters in ill-posed problems -- properties and limitations, Statistical M-estimation and consistency in large deformable models for image warping, Fast learning rates for plug-in classifiers, Covering numbers for bounded variation functions, Shrinkage variable selection and estimation in proportional hazards models with additive structure and high dimensionality, Adaptation in log-concave density estimation, Direct importance estimation for covariate shift adaptation, Oracle posterior contraction rates under hierarchical priors, A penalized likelihood approach for efficiently estimating a partially linear additive transformation model with current status data, Spline estimator for simultaneous variable selection and constant coefficient identification in high-dimensional generalized varying-coefficient models, Global risk bounds and adaptation in univariate convex regression, Set structured global empirical risk minimizers are rate optimal in general dimensions, Worst-case estimation for econometric models with unobservable components, The estimation for the general additive-multiplicative hazard model using the length-biased survival data, Sampling discretization of integral norms, Asymptotic normality of support vector machine variants and other regularized kernel methods, High-dimensional quantile varying-coefficient models with dimension reduction, Convergence rates for smoothing spline estimators in varying coefficient models, High-dimensional additive modeling, Nonparametric Bayesian estimation of a Hölder continuous diffusion coefficient, Bernstein-von Mises theorems for statistical inverse problems. I: Schrödinger equation, Nonparametric regression in nonstandard spaces, A non-asymptotic approach for model selection via penalization in high-dimensional mixture of experts models, Convex models, MLE and misspecification, Rates of convergence of posterior distributions., A sieve stochastic gradient descent estimator for online nonparametric regression in Sobolev ellipsoids, An asymptotic theory for the nonparametric maximum likelihood estimator in the Cox gene model
